rebuilding dematel threshold value an example of a food and beverage information system

Rebuilding DEMATEL threshold value: an example of a food and beverage information system Yi‑Fang Hsieh1*, Yu‑Cheng Lee2 and Shao‑Bin Lin3 Background The decision-making trial and evalua

Rebuilding DEMATEL threshold value: an

example of a food and beverage

information system

Yi‑Fang Hsieh1*, Yu‑Cheng Lee2 and Shao‑Bin Lin3

Background

The decision-making trial and evaluation laboratory (DEMATEL) method can be applied to solve complicated problems It operates mainly through collection of experts’ opinions by viewing the degree of influence between elements, the use of matrix oper-ations to obtain a causal reloper-ationship between the elements, and the establishment of similar structural equation modeling network diagrams The core DEMATEL method comprises four calculation steps: (1) define the scale; (2) build a direct-relation matrix;

(3) calculate a normalized matrix; (4) calculate a direct/indirect relationship matrix T

The threshold value is set after Step (4) The setting of a threshold value is typically influ-enced by problem complexity and divergent expert opinions

Some researchers use various methods to set up the threshold value, whereas some ignore explanations about the threshold value setting (Li and Tzeng 2009; Hu et al 2011; Lee et  al 2013) However, an overly high threshold value inappropriately reduces the

Abstract

This study demonstrates how a decision‑making trial and evaluation laboratory (DEMA‑ TEL) threshold value can be quickly and reasonably determined in the process of com‑ bining DEMATEL and decomposed theory of planned behavior (DTPB) models Models are combined to identify the key factors of a complex problem This paper presents a case study of a food and beverage information system as an example The analysis of the example indicates that, given direct and indirect relationships among variables, if

a traditional DTPB model only simulates the effects of the variables without consider‑ ing that the variables will affect the original cause‑and‑effect relationships among the variables, then the original DTPB model variables cannot represent a complete relationship For the food and beverage example, a DEMATEL method was employed

to reconstruct a DTPB model and, more importantly, to calculate reasonable DEMATEL threshold value for determining additional relationships of variables in the original DTPB model This study is method‑oriented, and the depth of investigation into any individual case is limited Therefore, the methods proposed in various fields of study should ideally be used to identify deeper and more practical implications

Keywords: Decision‑making trial and evaluation laboratory (DEMATEL), Threshold

value, Fractional factorial design, Decomposed theory of planned behavior model (DTPB model), Food and beverage information system

Open Access

© 2016 The Author(s) This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

METHODOLOGY

*Correspondence:

hsiehyifang@gmail.com

1 Department of Food

and Beverage Management,

Taipei College of Maritime

Technology, No 212, Sec 9,

Yanping N Rd., Shilin Dist.,

Taipei City 111, Taiwan, ROC

Full list of author information

is available at the end of the

article

significance of expert opinions and oversimplifies the problem, whereas an exceedingly

low threshold value results in divergent opinions and a lack of focus Therefore, if a

threshold value cannot appropriately differentiate expert opinions, it cannot accurately

present the critical factors of a complex problem

To determine a conventional threshold value purely using expert opinions or researcher judgments and to prevent inappropriate threshold value from affecting the

definitions of problems, some scholars studied the setting of DEMATEL threshold value

For instance, Li and Tzeng (2009) proposed a maximum mean de-entropy algorithms

(MMDE) to determine threshold value MMDE was mainly used to decide whether a

node is suitable to express in the impact-relations map However, in the past, operating

with subjective expert opinions, DEMATEL was unable to find appropriate threshold

value Even though some scholars proposed the MMDE method, that method did not

alleviate the problem of computational complexity

Therefore, the study proposes a type of simple and reasonable method to set threshold value The concept of fractional factorial design was expected to enable scientific

DEM-ATEL threshold value and to avoid subjective DEMDEM-ATEL threshold value

The present author is currently teaching university classes about dining information systems In addition to a food service worker’s typical professional skills, a crucial skill

valued by the food service job market is the ability to think systematically and to

con-trol work-related information flows to maximize efficiency The introduction of food and

beverage information system can greatly improve the quality of a food and beverage

ser-vice However, the improvement in service quality triggered by the information system

depends heavily on whether the workers make the most of the system In this study, the

decomposed theory of planned behavior (DTPB) proposed by Taylor and Todd (1995) is

adopted to examine the behaviors and inclinations of dining service workers in using a

food and beverage information system A new method is proposed to determine

DEM-ATEL threshold value and to explain the behaviors and inclinations of dining service

workers in using the food and beverage information system

This paper discusses the importance of the reasonable calculation of DEMATEL threshold value using the example of a food and beverage system Subsequently, the

DTPB information model theory that is used in this study is described The proposed

calculation steps and fractional factorial designs provide a reasonable and quick way

to calculate DEMATEL threshold value A food and beverage information system is

planned by combining DEMATEL and DTPB model to discover the behaviors and

incli-nations of dining service workers in using the food and beverage information system

This paper argues for conclusions and notes limitations of the present work

Literature review

Theory of planned behavior

In the theory of reasoned action (TRA), an individual behavior proceeds from free will

and an individual can completely determine whether to execute a behavior (Fishbein

and Ajzen 1975) However, apart from situations of free will, the expression of some

behaviors also requires the coordination of resources and opportunities during

execu-tion of those behaviors; for example, whether an individual possesses abilities for

behav-ioral control and implementation can affect his or her behavbehav-ioral intention (BI); and

individual ability to control this is called perceived behavioral control (PBC) Therefore,

Ajzen (1985) revised the TRA by adding PBC Ajzen held that when predicting BI, one

can delve into behavioral attitudes and subjective norms (SNs), but whether an

indi-vidual has the opportunities and resources to execute the behaviors in question and

whether the individual is able to control these behaviors, affects BI; this theory is the

theory of planned behavior (TPB) Its framework is shown in Fig. 1

Decomposed theory of planned behavior

Taylor and Todd (1995) proposed the DTPB model to explain human behavior regarding

information technology DTPB model was founded on the original TPB and

Technol-ogy Acceptance Model (TAM) DTPB adds creative characteristics in order to establish

three aspects that influence behaviors and inclinations, namely attitude, SN, and PBC

Their study indicated that the predictions of DTPB model were slightly more accurate

than TAM and TPB DTPB model had more explanatory power This can be explained

as follows:

(1) Actual behavior: This is an individual’s intention to perform a behavior which is a function of attitude toward behavior, subjective norms, and PBC

(2) BI: BI refers to the tendency of individuals to engage in some particular behavior

(3) Attitude: Attitude refers to the individual performance of specific acts held positive

or negative rating (4) SN: SN refers to an individual when the performance of a particular behavior, that affect them essential concerns, social pressure to support or not

(5) PBC: PBC refers to the degree of personal performance when a particular behavior, self-control resources

Taylor and Todd (1995) wrote that attitude can be derived from the perceived char-acteristics of an innovation Three charchar-acteristics of information technology

accept-ance and use are relative advantage, complexity, and compatibility (Moore and Benbasat

1991) Relative advantage refers to the benefits of innovative practices relative to the

original level Complexity refers to difficulties in the understanding, learning, and

aware-ness of the innovative technology

Taylor and Todd (1995) wrote that the definition of relative advantage and complex-ity are similar to the ideas of perceived usefulness (PU) and perceived ease of use (PEU)

in the TAM model Compatibility refers to innovation in line with the current value of

Attitude Toward Behavior

Behavioral Intention

Subject Norm

Actual Behavior Perceived

Behavioral Control

Fig 1 Theory of planned behavior model

potential recipient, the extent of past experience, and current needs To the notions of

PU and PEU can be added the notion of compatibility Attitude can be expressed as the

following three variables (Rogers 1983; Davis 1989):

(6) PU: the subjective belief of the user that the use of a particular information tech-nology will increase the level of his or her job performance

(7) PEU: the subjective belief of the user that the use of the Information Technology investment will not require significant effort and energy

(8) Compatibility: this is the perception of an individual that the innovative behaviors adopted match previous experience, current value, and needs; the more compat-ible the innovation is, the more chance it has of being adopted

In terms of SNs, Taylor and Todd (1995) pointed out three kinds of referent groups, peers, superiors, and subordinates In this study, SN can be broken into the following

two variables:

(9) Peer influence: when an individual is engaged in a certain behavior, positive inputs from his or her peers, such as friends and coworkers, increase the probability that

he or she continues the behavior

(10) Superior influence: this means that positive inputs from a worker’s supervisor regarding a behavior make it more likely that the worker continues the behavior

(11) PBC is divided into the following three variables (Bandura 1977):

(12) Self-efficacy: this means that when an individual perceives that he or she is capa-ble of a certain behavior, it is more likely that he or she engages in that particular behavior

(13) Resource facilitating conditions: these refer to the availability of the resources needed to facilitate a behavior when an individual is engaged in this behavior The resources can be time, money, equipment, and so on

(14) Technological facilitating conditions: these mean that when an individual believes that he or she has sufficient time, money, equipment, or other resources for a cer-tain behavior as well as the technical capability of engaging in such a behavior, it is more likely that he or she executes the behavior The framework is shown in Fig. 2

Use of the DTPB model has several advantages First, we can understand the differ-ent facets of anteceddiffer-ents in the DTPB model (Bagozzi 1981; Shimp and Kavas 1984)

Second, because of DTPB’s decomposed structure, the relationships between the

vari-ous factors and facets are clear and easy to understand, and therefore DTPB model can

explain the factors that may affect actual use (Mathieson 1991)

In previous DTPB model studies, structural equation modeling was used to analyze the relationships between variables (Shih and Fang 2004; Lin 2007; Malek et al 2010)

However, accurate analysis was difficult because incorrect conclusions were often caused

by some variables that did not satisfy the assumption of independence To solve this, Lee

et al (2013) employed the expert-opinion-oriented DEMATEL to reestablish the causal

relationships between DTPB variables and their mutual influences Despite the efforts to

reestablish the causal relationships between DTPB variables and their mutual influences

using the DEMATEL method, this method was dependent on expert opinions regarding

the degrees of influence between elements In particular, a clear definition of threshold

value was still missing in the DEMATEL method

DEMATEL threshold value

DEMATEL was built by the Battelle Geneva Institute to solve difficult problems (Gabus

and Fontela 1973; Fontela and Gabus 1976) It was intended to find direct and indirect

relationships, and to gauge strength of influence between different elements in the

com-plex environment

Recently, the DEMATEL has been widely introduced to identify key factors in com-plicated problems For instance, Wang et al (2016) sought to identify the key barriers

to the implementation of green supply chain management in the packaging industry by

using DEMATEL Asad et al (2016) attempted to study the key factors affecting

cus-tomer satisfaction in an internet banking system so that bank operations might be

pri-oritized to reflect cause and effect relationships Pan and Ngnyen (2015) proposed an

approach for helping manufacturing companies identify the key performance

evalua-tion criteria for achieving customer satisfacevalua-tion through balanced scorecard (BSC) and

multiple criteria decision-making (MCDM) approaches Uygun et al (2015) integrated

DEMATEL and fuzzy ANP techniques for evaluation and selection of outsourcing

pro-viders for a telecommunication company Lu et al (2013) improved RFID adoption in

Taiwan’s healthcare industry using a DEMATEL technique with a hybrid MCDM model

Attitude Toward Behavior

Behavioral Intention

Subject Norm Actual

Behavior

Perceived Behavioral Control

Perceived Usefulness

Compatibility

Perceived Ease of Use

Peer Influence

Superior Influence Self-efficacy

Resource Facilitation Condition Technology Facilitation Condition

Fig 2 Decomposed theory of planned behavior model

Lee et al (2010) applied fuzzy DEMATEL to the TAM to verify benefits These

DEMA-TEL-related studies suggest that this approach has been extensively adopted in various

fields of study and widely accepted

Briefly, the procedure of DEMATEL can be implemented as follows:

Step 1 Define the evaluation scale

Define the evaluation scale to show the degree of impact Values on the 10-point scale represent degrees of influence from “no influence” to “great influence”

Step 2 Build a direct-relation matrix

A direct-relation matrix X is produced by integrating the opinions of experts, where x ij expresses the extent to which xi affects xj; the value of any element

on the diagonal is 0

Step 3 Normalize the direct-relation matrix

A direct-relation matrix is normalized with matrix X, using the following

method:

Step 4 Calculate a direct/indirect relationship matrix T

Because the normalized matrix N is known, the following equation can pro-duce the total matrix T:

where I is an identity matrix.

Fractional factorial design is typically applied in experiments for developing new products and improving existing production methods The success of such

experi-ments depends on factor configuration before the experiment and effect analysis after

the experiment To reduce experimental cost, time, and complexity, it is crucial that no

significant factors be excluded Numerous studies have addressed this problem, most of

which have adopted the effect-sparsity assumption proposed by Box and Meyer (1986)

The effect-sparsity assumption is that among the various effects, only a few are

signif-icant Regarding this assumption, several scholars have written that significant effects

can be treated as outliers, which are cut off from samples, and no outlier effects can be

adopted for estimation of experimental errors (Lenth 1989; Schneider et al 1993;

Haal-and Haal-and O’Connell 1995)

Generally, when an experimental design involves numerous factors, a screening experiment should be conducted first, in which crucial factors that exert effects on

response variables are discovered The crucial factors can then be selected to undergo an

(1)

X =







0 x12 · · · x1n

x21 0 · · · x2n

..

xn1 xn2 · · · 0







(2)

Max

1≤ i ≤ n

n j=1xij

and N = X

(3)

T = lim k→∞

N + N2+ · · · + Nk

= N (I − N )−1

optimization experiment for determining their optimal input levels However, because

of limited experimental resources, unreplicated factorial design is typically adopted in

screening experiments and no significant effects are eliminated Consequently, when

the data of such experiments are analyzed with no degree of freedom left for

estimat-ing experimental errors, traditional t tests and F tests cannot be adopted to determine

the significance of effects To solve this problem, several scholars have proposed various

analytical methods Daniel (1959) was the first to investigate this problem, and

numer-ous scholars have developed distinct statistical methods based on the fractional factorial

design to identify which effects are influential Among these scholars, Lenth (1989)

pro-posed the effect-sparsity assumption, based on the research of Box and Meyer (1986)

This assumption indicates that only a few factorial effects have specific influences on

response variables Therefore, a censoring approach and pseudostandard errors are

employed to estimate the standard deviations of effects; these can lead to statistics

simi-lar to those of t tests The threshold value from this method are then adopted to

deter-mine effect significance Because the calculations required for the method proposed

by Lenth are relatively simple, this method is widely applied in unreplicated factorial

designs for analyzing test data

Based on the effect-sparsity assumption, the method proposed by Lenth (1989) estimates

τ by assuming that the median of 

ˆ

βk

 equals

2

3τ when H0: β1= · · · = βm= 0 Initially, because median

1 ≤k≤m

 ˆ

βk

≈ 0.67τ the initial estimate of τ is defined as S0= 1.5 ×1median≤k≤m

 ˆ

βk

 Subsequently, because Pr=βˆk

≥ 2.5τ ||β1= · · · = βm= 0| ≈ 0.01, Lenth consid-ered that estimating τ using the 

ˆ

βk

 value that are smaller than 2.5S0 should generate relatively robust estimates Consequently, Lenth defined pseudostandard error (PSE) as

PSE= 1.5 × median

 ˆ

βk

 <2.5S 0

 ˆ

βk

 where

median

 ˆ

βk

 <2.5S 0

 ˆ

βk

 denotes the median generated from the absolute regression coefficients that are smaller than 2.5S0 In other words, PSE

repre-sents the S0 established after the regression coefficients that are possible active effects

have been deleted Subsequently, Lenth defined the margin of error (ME) of various

regression coefficients as ME = t1− α

2 ; m

3 × PSE and adopted ME value to test effect sig-nificance In the equation, t1 −α2 ;m3 represents the quartile of 1 −α

2

in a t distribution

where the degree of freedom is m

3 Finally, Lenth suggested that the effects corresponding

to the absolute regression coefficients that are less than or equal to ME value should be

regarded as nonsignificant The calculation steps of Lenth’s method are as follows:

Step 1 Calculate S0 the initial value of τ

Step 2 Calculate PSE

Step 3 Calculate ME

(4)

S0= 1.5 × median

1≤ k ≤ m

 ˆ

βk

(5) PSE= 1.5 × median

βˆk

< 2.5S0

 ˆ

βk

DEMATEL threshold value is set based on Lenth’s principles of distinguishing effect significance, whereby threshold value and ME are adopted to eliminate nonsignificant

factors for obtaining factors with significant influences in scenarios with complex

prob-lems or factors When Lenth’s method is combined with the DEMATEL method,

suit-able threshold value can be determined by calculating ME value, and problems resulting

from inappropriate DEMATEL threshold value can be effectively resolved

Example: food and beverage information system in DTPB model

Research design

This objective of this study is to demonstrate how the DEMATEL threshold value can

be quickly and reasonably determined by combining DEMATEL and DTPB models to

identify the key factors in a complex problem A food and beverage information system

is presented as an example The combination of DEMATEL and DTPB models as applied

to the food and beverage information system were analyzed to discover the behaviors

and inclinations of dining service workers regarding use of the food and beverage

infor-mation system These findings should contribute to the further introduction of food

and beverage information systems and the improvement of food and beverage service

quality

In this study, a fractional factorial design was employed to build DEMATEL threshold value to obtain critical factors of a complex system Invitations were issued to 20 experts,

who were asked to share their insights on the use of a DEMATEL-DTPB combination

for the analysis of worker behaviors relevant to a food and beverage information

sys-tem These experts, who answered the questionnaires developed for this study, included

restaurant owners, waiters who have direct contact with customers, and college faculty

members who teach the theory of food and beverage information systems The

ques-tionnaire survey was administered between October and December of 2015 There were

more males than females among these 20 experts More than half of the experts had a

college degree or a postgraduate degree The majority of the experts were between 40

and 50 years of age The survey included face-to-face interviews with the experts The

questions provided in the questionnaire were based on food and beverage information

systems The interviewees were asked to estimate the degree of influence on the variables

of the DTPB model based on their knowledge regarding the system A 10-point scale

was introduced to rate the degree of influence from “no influence” to “great influence.”

The original DTPB model has 13 variables: PU (A1), PEU (A2), compatibility (A3), peer influence (A4), superior influence (A5), self-efficacy (A6), resource facilitation conditions

(A7), technology facilitation conditions (A8), attitude toward behavior (A9), SN (A10),

PBC (A11), BI (A12), and actual behavior (A13)

Data analysis

Based on the analysis procedures of DEMATEL, a direct relationship matrix X was first

established, based on the opinions of the aforementioned 20 professionals, to adopt the

mean and establish a direct relationship matrix X according to Eq. (1), which is shown in

Table 1

(6)

ME= t1 −α2;m3 × PSE

In Eq. (2), a normalized direct-relation matrix, wherein the sum of the row vector was used as the normalized basis, produced the value 1/(8 + 1+1 + 1+1 + 1+10 + 1+1 + 1

+1) = 1/27 The normalized direct-relation matrix is shown in Table 2

After normalization, the direct/indirect relationship matrix T was derived using

Eq. (3), as shown in Table 3

A more obvious cause-and-effect relationship was then determined The values of the direct/indirect relationship matrix table were set by a threshold value

Initially, a threshold value was calculated using Eq.  (4) The median (0.108) was

selected from the direct/indirect relationship matrix T The initial value of τ (S0) was

calculated as follows:

S0= 1.5 × 0.108 = 0.162

Table 1 Direct relationship matrix of  food and  beverage information system in  DTPB

model

A1 0 0 0 1 2 1 1 1 9 1 1 1 1

A2 8 0 0 1 1 1 1 1 10 1 1 1 1

A3 0 0 0 1 1 1 1 1 9 1 1 1 1

A4 0 0 0 0 0 1 1 1 1 9 1 1 1

A5 0 0 0 0 0 1 1 1 1 9 1 1 1

A6 1 1 1 1 1 0 1 1 1 1 10 1 1

A7 1 1 1 1 1 1 0 1 1 1 8 1 1

A8 1 1 1 1 1 1 1 0 1 1 7 1 1

A9 1 1 0 0 0 3 0 0 0 0 0 8 1

A10 0 0 0 0 0 0 2 2 0 0 0 8 1

A11 5 5 0 0 0 5 0 0 0 0 0 9 1

A12 0 0 1 0 0 1 1 1 1 0 0 0 10

A13 1 1 1 1 2 1 1 1 1 1 1 1 0

Table 2 Normalized matrix of food and beverage information system in DTPB model

A1 0.000 0.000 0.000 0.037 0.074 0.037 0.037 0.037 0.333 0.037 0.037 0.037 0.037

A2 0.296 0.000 0.000 0.037 0.037 0.037 0.037 0.037 0.370 0.037 0.037 0.037 0.037

A3 0.000 0.000 0.000 0.037 0.037 0.037 0.037 0.037 0.333 0.037 0.037 0.037 0.037

A4 0.000 0.000 0.000 0.000 0.000 0.037 0.037 0.037 0.037 0.333 0.037 0.037 0.037

A5 0.000 0.000 0.000 0.000 0.000 0.037 0.037 0.037 0.037 0.333 0.037 0.037 0.037

A6 0.037 0.037 0.037 0.037 0.037 0.000 0.037 0.037 0.037 0.037 0.370 0.037 0.037

A7 0.037 0.037 0.037 0.037 0.037 0.037 0.000 0.037 0.037 0.037 0.296 0.037 0.037

A8 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.000 0.037 0.037 0.259 0.037 0.037

A9 0.037 0.037 0.000 0.000 0.000 0.111 0.000 0.000 0.000 0.000 0.000 0.296 0.037

A10 0.000 0.000 0.000 0.000 0.000 0.000 0.074 0.074 0.000 0.000 0.000 0.296 0.037

A11 0.185 0.185 0.000 0.000 0.000 0.185 0.000 0.000 0.000 0.000 0.000 0.333 0.037

A12 0.000 0.000 0.037 0.000 0.000 0.037 0.037 0.037 0.037 0.000 0.000 0.000 0.370

A13 0.037 0.037 0.037 0.037 0.074 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.000

Using Eq. (5), after the values in the direct/indirect relationship matrix T that were greater than or equal to 2.5S0 had been deleted, the median (0.103) was obtained as

PSE= 1.5 × 0.103 = 0.1545

Finally, using Eq. (6), given α = 0.05 and df = 56, it was calculated that t1 − α

2 ;m3 = 2.0033 and ME = 2.0033 × 0.1545 = 0.310 An effect level lower than 0.310 was treated as

a relationship that was not causal A relationship matrix with a significant effect was

determined, as shown in Table 4

Table 4 not only gives the degrees of influence among the variables of the DTPB model after the integration of the DEMATEL and DTPB models, but also helps clarify the

new relationships among the variables that are apparent after the rebuilding of DTPB

model using the DEMATEL model combined with the analysis results with the original

DTPB model For example, some new relationships between variables were apparent, as

shown in Fig. 3 The influence coefficients of A1, A2, and A3 on A9 are 0.432, 0.597, and

0.427, respectively; the influence coefficients of A4 and A5 on A10 are 0.374 and 0.374,

Table 3 Direct/indirect matrix of food and beverage information system in DTPB model

A1 0.084 0.065 0.030 0.063 0.110 0.152 0.086 0.086 0.432 0.121 0.161 0.287 0.195

A2 0.407 0.085 0.039 0.082 0.106 0.200 0.109 0.109 0.597 0.144 0.208 0.377 0.254

A3 0.082 0.063 0.029 0.062 0.072 0.148 0.082 0.082 0.427 0.107 0.156 0.277 0.188

A4 0.062 0.048 0.027 0.022 0.031 0.106 0.095 0.095 0.113 0.374 0.142 0.253 0.172

A5 0.062 0.048 0.027 0.022 0.031 0.106 0.095 0.095 0.113 0.374 0.142 0.253 0.172

A6 0.216 0.167 0.074 0.077 0.094 0.178 0.103 0.103 0.239 0.134 0.526 0.369 0.245

A7 0.191 0.148 0.070 0.073 0.089 0.190 0.062 0.097 0.218 0.127 0.440 0.328 0.222

A8 0.179 0.138 0.068 0.071 0.086 0.179 0.094 0.059 0.208 0.124 0.396 0.308 0.210

A9 0.104 0.080 0.033 0.026 0.038 0.181 0.046 0.046 0.111 0.048 0.111 0.409 0.219

A10 0.052 0.040 0.033 0.023 0.033 0.064 0.113 0.113 0.078 0.042 0.101 0.390 0.207

A11 0.343 0.265 0.052 0.055 0.079 0.324 0.085 0.085 0.286 0.099 0.209 0.577 0.320

A12 0.069 0.053 0.069 0.034 0.053 0.111 0.081 0.081 0.137 0.063 0.113 0.134 0.452

A13 0.108 0.083 0.058 0.060 0.106 0.113 0.079 0.079 0.151 0.116 0.142 0.193 0.112

Table 4 Significant direct/indirect matrix of  food and  beverage information system

in DTPB model (rebuild threshold value = 0.310)

A2 0.407 0.597 0.377

A11 0.343 0.324 0.577 0.320

A